




'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()





class      lowerCAmelCase (			unittest.TestCase):
       __lowercase	:   Any										=      StableDiffusionLDMaDPipeline
       __lowercase	:   Dict										=      TEXT_TO_IMAGE_PARAMS
       __lowercase	:   Optional[Any]										=      TEXT_TO_IMAGE_BATCH_PARAMS
       __lowercase	:   int										=      TEXT_TO_IMAGE_IMAGE_PARAMS


       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            torch.manual_seed(0	)
            __snake_case            =	UNetaDConditionModel(
                block_out_channels=(32, 64)     ,	layers_per_block=2     ,	sample_size=32     ,	in_channels=4     ,	out_channels=4     ,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,	cross_attention_dim=32     ,	)
            __snake_case            =	DDIMScheduler(
                beta_start=0.00_085     ,	beta_end=0.012     ,	beta_schedule='''scaled_linear'''     ,	clip_sample=__SCREAMING_SNAKE_CASE     ,	set_alpha_to_one=__SCREAMING_SNAKE_CASE     ,	)
            torch.manual_seed(0	)
            __snake_case            =	AutoencoderKL(
                block_out_channels=[32, 64]     ,	in_channels=6     ,	out_channels=6     ,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,	latent_channels=4     ,	)
            torch.manual_seed(0	)
            __snake_case            =	CLIPTextConfig(
                bos_token_id=0     ,	eos_token_id=2     ,	hidden_size=32     ,	intermediate_size=37     ,	layer_norm_eps=1E-05     ,	num_attention_heads=4     ,	num_hidden_layers=5     ,	pad_token_id=1     ,	vocab_size=1000     ,	)
            __snake_case            =	CLIPTextModel(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

            __snake_case            =	{
                '''unet''': unet,
                '''scheduler''': scheduler,
                '''vae''': vae,
                '''text_encoder''': text_encoder,
                '''tokenizer''': tokenizer,
                '''safety_checker''': None,
                '''feature_extractor''': None,
            }
            return components


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=0	)   ->	Union[str, Any]:



            '''simple docstring'''




            if str(__SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
                 __snake_case            =	torch.manual_seed(__SCREAMING_SNAKE_CASE	)
            else:
                 __snake_case            =	torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	{
                '''prompt''': '''A painting of a squirrel eating a burger''',
                '''generator''': generator,
                '''num_inference_steps''': 2,
                '''guidance_scale''': 6.0,
                '''output_type''': '''numpy''',
            }
            return inputs


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator

            __snake_case            =	self.get_dummy_components()
            __snake_case            =	StableDiffusionLDMaDPipeline(**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ldmad_pipe.to(__SCREAMING_SNAKE_CASE	)
            ldmad_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ldmad_pipe(**__SCREAMING_SNAKE_CASE	)
            __snake_case       ,     __snake_case            =	output.rgb, output.depth

            __snake_case            =	rgb[0, -3:, -3:, -1]
            __snake_case            =	depth[0, -3:, -1]

            assert rgb.shape == (1, 64, 64, 3)
            assert depth.shape == (1, 64, 64)

            __snake_case            =	np.array(
                [0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262]	)
            __snake_case            =	np.array([103.46_727, 85.812_004, 87.849_236]	)

            assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb	).max() < 1E-2
            assert np.abs(image_slice_depth.flatten() - expected_slice_depth	).max() < 1E-2


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	self.get_dummy_components()
            __snake_case            =	StableDiffusionLDMaDPipeline(**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ldmad_pipe.to(__SCREAMING_SNAKE_CASE	)
            ldmad_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	3 * [inputs['''prompt''']]

            # forward
            __snake_case            =	ldmad_pipe(**__SCREAMING_SNAKE_CASE	)
            __snake_case       ,     __snake_case            =	output.rgb, output.depth
            __snake_case            =	rgb_slice_a[0, -3:, -3:, -1]
            __snake_case            =	depth_slice_a[0, -3:, -1]

            __snake_case            =	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	3 * [inputs.pop('''prompt'''	)]

            __snake_case            =	ldmad_pipe.tokenizer(
                __SCREAMING_SNAKE_CASE     ,	padding='''max_length'''     ,	max_length=ldmad_pipe.tokenizer.model_max_length     ,	truncation=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''     ,	)
            __snake_case            =	text_inputs['''input_ids'''].to(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	ldmad_pipe.text_encoder(__SCREAMING_SNAKE_CASE	)[0]

            __snake_case            =	prompt_embeds

            # forward
            __snake_case            =	ldmad_pipe(**__SCREAMING_SNAKE_CASE	)
            __snake_case       ,     __snake_case            =	output.rgb, output.depth
            __snake_case            =	rgb_slice_a[0, -3:, -3:, -1]
            __snake_case            =	depth_slice_a[0, -3:, -1]

            assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()	).max() < 1E-4
            assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()	).max() < 1E-4




       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
            __snake_case            =	self.get_dummy_components()
            __snake_case            =	PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	StableDiffusionLDMaDPipeline(**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ldmad_pipe.to(__SCREAMING_SNAKE_CASE	)
            ldmad_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	'''french fries'''
            __snake_case            =	ldmad_pipe(**__SCREAMING_SNAKE_CASE     ,	negative_prompt=__SCREAMING_SNAKE_CASE	)

            __snake_case       ,     __snake_case            =	output.rgb, output.depth
            __snake_case            =	rgb[0, -3:, -3:, -1]
            __snake_case            =	depth[0, -3:, -1]

            assert rgb.shape == (1, 64, 64, 3)
            assert depth.shape == (1, 64, 64)

            __snake_case            =	np.array(
                [0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217]	)
            __snake_case            =	np.array([107.84_738, 84.62_802, 89.962_135]	)
            assert np.abs(rgb_slice.flatten() - expected_slice_rgb	).max() < 1E-2
            assert np.abs(depth_slice.flatten() - expected_slice_depth	).max() < 1E-2





@slow
@require_torch_gpu
class      lowerCAmelCase (			unittest.TestCase):


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE="cpu"     ,	__SCREAMING_SNAKE_CASE=torch.floataa     ,	__SCREAMING_SNAKE_CASE=0	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	np.random.RandomState(__SCREAMING_SNAKE_CASE	).standard_normal((1, 4, 64, 64)	)
            __snake_case            =	torch.from_numpy(__SCREAMING_SNAKE_CASE	).to(device=__SCREAMING_SNAKE_CASE     ,	dtype=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	{
                '''prompt''': '''a photograph of an astronaut riding a horse''',
                '''latents''': latents,
                '''generator''': generator,
                '''num_inference_steps''': 3,
                '''guidance_scale''': 7.5,
                '''output_type''': '''numpy''',
            }
            return inputs




       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''	)
            __snake_case            =	ldmad_pipe.to(__SCREAMING_SNAKE_CASE	)
            ldmad_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_inputs(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ldmad_pipe(**__SCREAMING_SNAKE_CASE	)
            __snake_case       ,     __snake_case            =	output.rgb, output.depth
            __snake_case            =	rgb[0, -3:, -3:, -1].flatten()
            __snake_case            =	rgb[0, -3:, -1].flatten()

            assert rgb.shape == (1, 512, 512, 3)
            assert depth.shape == (1, 512, 512)

            __snake_case            =	np.array(
                [0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706]	)
            __snake_case            =	np.array(
                [0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706]	)
            assert np.abs(rgb_slice - expected_slice_rgb	).max() < 3E-3
            assert np.abs(depth_slice - expected_slice_depth	).max() < 3E-3





@nightly
@require_torch_gpu
class      lowerCAmelCase (			unittest.TestCase):


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE="cpu"     ,	__SCREAMING_SNAKE_CASE=torch.floataa     ,	__SCREAMING_SNAKE_CASE=0	)   ->	int:



            '''simple docstring'''




            __snake_case            =	torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	np.random.RandomState(__SCREAMING_SNAKE_CASE	).standard_normal((1, 4, 64, 64)	)
            __snake_case            =	torch.from_numpy(__SCREAMING_SNAKE_CASE	).to(device=__SCREAMING_SNAKE_CASE     ,	dtype=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	{
                '''prompt''': '''a photograph of an astronaut riding a horse''',
                '''latents''': latents,
                '''generator''': generator,
                '''num_inference_steps''': 50,
                '''guidance_scale''': 7.5,
                '''output_type''': '''numpy''',
            }
            return inputs


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''	).to(__SCREAMING_SNAKE_CASE	)
            ldmad_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_inputs(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ldmad_pipe(**__SCREAMING_SNAKE_CASE	)
            __snake_case       ,     __snake_case            =	output.rgb, output.depth

            __snake_case            =	0.495_586
            __snake_case            =	0.33_795_515
            __snake_case            =	112.48_518
            __snake_case            =	98.489_746
            assert np.abs(expected_rgb_mean - rgb.mean()	) < 1E-3
            assert np.abs(expected_rgb_std - rgb.std()	) < 1E-3
            assert np.abs(expected_depth_mean - depth.mean()	) < 1E-3
            assert np.abs(expected_depth_std - depth.std()	) < 1E-3




       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''	).to(__SCREAMING_SNAKE_CASE	)
            ldmad_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_inputs(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ldmad_pipe(**__SCREAMING_SNAKE_CASE	)
            __snake_case       ,     __snake_case            =	output.rgb, output.depth

            __snake_case            =	0.4_194_127
            __snake_case            =	0.35_375_586
            __snake_case            =	0.5_638_502
            __snake_case            =	0.34_686_103

            assert rgb.shape == (1, 512, 512, 3)
            assert depth.shape == (1, 512, 512, 1)
            assert np.abs(expected_rgb_mean - rgb.mean()	) < 1E-3
            assert np.abs(expected_rgb_std - rgb.std()	) < 1E-3
            assert np.abs(expected_depth_mean - depth.mean()	) < 1E-3
            assert np.abs(expected_depth_std - depth.std()	) < 1E-3










'''simple docstring'''


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCAmelCase_						:  Optional[Any]				  =						16
UpperCAmelCase_						:  List[str]				  =						32







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__ = 16				, SCREAMING_SNAKE_CASE__ = "bert-base-cased"	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			List[str]							= load_dataset("""glue"""				, """mrpc"""	)

				def tokenize_function(SCREAMING_SNAKE_CASE__	):
								# max_length=None => use the model max length (it's actually the default)
								_SCREAMING_SNAKE_CASE					:			List[str]							= tokenizer(examples["""sentence1"""]				, examples["""sentence2"""]				, truncation=SCREAMING_SNAKE_CASE__				, max_length=SCREAMING_SNAKE_CASE__	)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				_SCREAMING_SNAKE_CASE					:			str							= datasets.map(
				    SCREAMING_SNAKE_CASE__				, batched=SCREAMING_SNAKE_CASE__				, remove_columns=["""idx""", """sentence1""", """sentence2"""]				, load_from_cache_file=SCREAMING_SNAKE_CASE__	)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				_SCREAMING_SNAKE_CASE					:			Tuple							= tokenized_datasets.rename_column("""label"""				, """labels"""	)

				def collate_fn(SCREAMING_SNAKE_CASE__	):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(SCREAMING_SNAKE_CASE__				, padding="""max_length"""				, max_length=128				, return_tensors="""pt"""	)
								return tokenizer.pad(SCREAMING_SNAKE_CASE__				, padding="""longest"""				, return_tensors="""pt"""	)

				# Instantiate dataloaders.
				_SCREAMING_SNAKE_CASE					:			int							= DataLoader(
				    tokenized_datasets["""train"""]				, shuffle=SCREAMING_SNAKE_CASE__				, collate_fn=SCREAMING_SNAKE_CASE__				, batch_size=SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Any							= DataLoader(
				    tokenized_datasets["""validation"""]				, shuffle=SCREAMING_SNAKE_CASE__				, collate_fn=SCREAMING_SNAKE_CASE__				, batch_size=SCREAMING_SNAKE_CASE__	)

				return train_dataloader, eval_dataloader







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				model.eval()
				_SCREAMING_SNAKE_CASE					:			Dict							= 0
				for step, batch in enumerate(SCREAMING_SNAKE_CASE__	):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device	)
								with torch.no_grad():
												_SCREAMING_SNAKE_CASE					:			Tuple							= model(**SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= outputs.logits.argmax(dim=-1	)
								# It is slightly faster to call this once, than multiple times
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= accelerator.gather(
								    (predictions, batch["""labels"""])	)  # If we are in a multiprocess environment, the last batch has duplicates
								if accelerator.use_distributed:
												if step == len(SCREAMING_SNAKE_CASE__	) - 1:
																_SCREAMING_SNAKE_CASE					:			List[str]							= predictions[: len(eval_dataloader.dataset	) - samples_seen]
																_SCREAMING_SNAKE_CASE					:			List[Any]							= references[: len(eval_dataloader.dataset	) - samples_seen]
												else:
																samples_seen += references.shape[0]
								metric.add_batch(
								    predictions=SCREAMING_SNAKE_CASE__				, references=SCREAMING_SNAKE_CASE__				, )

				_SCREAMING_SNAKE_CASE					:			str							= metric.compute()
				return eval_metric["accuracy"]







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= config["""lr"""]
				_SCREAMING_SNAKE_CASE					:			Any							= int(config["""num_epochs"""]	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= int(config["""seed"""]	)
				_SCREAMING_SNAKE_CASE					:			Tuple							= int(config["""batch_size"""]	)
				_SCREAMING_SNAKE_CASE					:			List[str]							= args.model_name_or_path

				set_seed(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= get_dataloaders(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__				, return_dict=SCREAMING_SNAKE_CASE__	)

				# Instantiate optimizer
				_SCREAMING_SNAKE_CASE					:			List[str]							= (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				_SCREAMING_SNAKE_CASE					:			Any							= optimizer_cls(params=model.parameters()				, lr=SCREAMING_SNAKE_CASE__	)

				if accelerator.state.deepspeed_plugin is not None:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 1
				_SCREAMING_SNAKE_CASE					:			int							= (len(SCREAMING_SNAKE_CASE__	) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								_SCREAMING_SNAKE_CASE					:			List[Any]							= get_linear_schedule_with_warmup(
								    optimizer=SCREAMING_SNAKE_CASE__				, num_warmup_steps=0				, num_training_steps=SCREAMING_SNAKE_CASE__				, )
				else:
								_SCREAMING_SNAKE_CASE					:			Any							= DummyScheduler(SCREAMING_SNAKE_CASE__				, total_num_steps=SCREAMING_SNAKE_CASE__				, warmup_num_steps=0	)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Tuple							= accelerator.prepare(
				    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				# We need to keep track of how many total steps we have iterated over
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 0
				# We also need to keep track of the stating epoch so files are named properly
				_SCREAMING_SNAKE_CASE					:			str							= 0
				_SCREAMING_SNAKE_CASE					:			Tuple							= evaluate.load("""glue"""				, """mrpc"""	)
				_SCREAMING_SNAKE_CASE					:			int							= num_epochs

				if args.partial_train_epoch is not None:
								_SCREAMING_SNAKE_CASE					:			Dict							= args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint	)
								_SCREAMING_SNAKE_CASE					:			Any							= args.resume_from_checkpoint.split("""epoch_"""	)[1]
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= """"""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								_SCREAMING_SNAKE_CASE					:			int							= int(SCREAMING_SNAKE_CASE__	) + 1
								_SCREAMING_SNAKE_CASE					:			List[str]							= evaluation_loop(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								accelerator.print("""resumed checkpoint performance:"""				, SCREAMING_SNAKE_CASE__	)
								accelerator.print("""resumed checkpoint's scheduler's lr:"""				, lr_scheduler.get_lr()[0]	)
								accelerator.print("""resumed optimizers's lr:"""				, optimizer.param_groups[0]["""lr"""]	)
								with open(os.path.join(args.output_dir				, f"""state_{starting_epoch-1}.json"""	)				, """r"""	) as f:
												_SCREAMING_SNAKE_CASE					:			Any							= json.load(SCREAMING_SNAKE_CASE__	)
												assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["lr"] == lr_scheduler.get_lr()[0]
												), "Scheduler learning rate mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
												), "Optimizer learning rate mismatch, loading from checkpoint failed"
												assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
												return

    # Now we train the model
				_SCREAMING_SNAKE_CASE					:			int							= {}
				for epoch in range(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):
								model.train()
								for step, batch in enumerate(SCREAMING_SNAKE_CASE__	):
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= model(**SCREAMING_SNAKE_CASE__	)
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= outputs.loss
												_SCREAMING_SNAKE_CASE					:			int							= loss / gradient_accumulation_steps
												accelerator.backward(SCREAMING_SNAKE_CASE__	)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								_SCREAMING_SNAKE_CASE					:			int							= f"""epoch_{epoch}"""
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= os.path.join(args.output_dir				, SCREAMING_SNAKE_CASE__	)
								accelerator.save_state(SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= evaluation_loop(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Dict							= accuracy
								_SCREAMING_SNAKE_CASE					:			Any							= lr_scheduler.get_lr()[0]
								_SCREAMING_SNAKE_CASE					:			Any							= optimizer.param_groups[0]["""lr"""]
								_SCREAMING_SNAKE_CASE					:			Dict							= epoch
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= overall_step
								accelerator.print(f"""epoch {epoch}:"""				, SCREAMING_SNAKE_CASE__	)

								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												with open(os.path.join(args.output_dir				, f"""state_{epoch}.json"""	)				, """w"""	) as f:
																json.dump(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[str]							= argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""	)
				parser.add_argument(
				    """--model_name_or_path"""				, type=SCREAMING_SNAKE_CASE__				, default="""bert-base-cased"""				, help="""Path to pretrained model or model identifier from huggingface.co/models."""				, required=SCREAMING_SNAKE_CASE__				, )
				parser.add_argument(
				    """--output_dir"""				, type=SCREAMING_SNAKE_CASE__				, default="""."""				, help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				, )
				parser.add_argument(
				    """--resume_from_checkpoint"""				, type=SCREAMING_SNAKE_CASE__				, default=SCREAMING_SNAKE_CASE__				, help="""If the training should continue from a checkpoint folder."""				, )
				parser.add_argument(
				    """--partial_train_epoch"""				, type=SCREAMING_SNAKE_CASE__				, default=SCREAMING_SNAKE_CASE__				, help="""If passed, the training will stop after this number of epochs."""				, )
				parser.add_argument(
				    """--num_epochs"""				, type=SCREAMING_SNAKE_CASE__				, default=2				, help="""Number of train epochs."""				, )
				_SCREAMING_SNAKE_CASE					:			str							= parser.parse_args()
				_SCREAMING_SNAKE_CASE					:			int							= {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

				training_function(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				main()




